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Selectionism and Learning in Projects with Complexity and Unforeseeable Uncertainty


  • Svenja C. Sommer

    () (Krannert School of Management, Purdue University, West State Street, West Lafayette, Indiana 47907)

  • Christoph H. Loch

    () (HP Labs, and INSEAD, Technology Management, Boulevard de Constance, 77305 Fontainebleau, Cedex, France)


Companies innovating in dynamic environments face the combined challenge of unforeseeable uncertainty (the inability to recognize the relevant influence variables and their functional relationships; thus, events and actions cannot be planned ahead of time) and high complexity (large number of variables and interactions; this leads to difficulty in assessing optimal actions beforehand). There are two fundamental strategies to manage innovation with unforeseeable uncertainty and complexity: trial and error learning and selectionism. Trial and error learning involves a flexible (unplanned) adjustment of the considered actions and targets to new information about the relevant environment as it emerges. Selectionism involves pursuing several approaches independently of one another and picking the best one ex post. Neither strategy nor project management literatures have compared the relative advantages of the two approaches in the presence of unforeseeable uncertainty and complexity. We build a model of a complex project with unforeseeable uncertainty, simulating problem solving as a local search on a rugged landscape. We compare the project payoff performance under trial and error learning and selectionism, based on a priori identifiable project characteristics: whether unforeseeable uncertainty is present, how high the complexity is, and how much trial and error learning and parallel trials cost. We find that if unforeseeable uncertainty is present and the team cannot run trials in a realistic user environment (indicating the project's true market performance), trial and error learning is preferred over selectionism. Moreover, the presence of unforeseeable uncertainty can reverse an established result from computational optimization: Without unforeseeable uncertainty, the optimal number of parallel trials increases in complexity. But with unforeseeable uncertainty, the optimal number of trials might decrease because the unforeseeable factors make the trials less and less informative as complexity grows.

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  • Svenja C. Sommer & Christoph H. Loch, 2004. "Selectionism and Learning in Projects with Complexity and Unforeseeable Uncertainty," Management Science, INFORMS, vol. 50(10), pages 1334-1347, October.
  • Handle: RePEc:inm:ormnsc:v:50:y:2004:i:10:p:1334-1347

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    References listed on IDEAS

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    1. Mangematin, V. & Blanco, S. & Deschamp, B. & Genet, C. & Kahane, B., 2006. "Project management : learning by breaking the rules," Working Papers 200604, Grenoble Applied Economics Laboratory (GAEL).
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    5. Wenqiang Xiao & Yi Xu, 2012. "The Impact of Royalty Contract Revision in a Multistage Strategic R&D Alliance," Management Science, INFORMS, vol. 58(12), pages 2251-2271, December.
    6. Feduzi, Alberto & Runde, Jochen, 2014. "Uncovering unknown unknowns: Towards a Baconian approach to management decision-making," Organizational Behavior and Human Decision Processes, Elsevier, vol. 124(2), pages 268-283.
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    8. Gil, Nuno & Tether, Bruce S., 2011. "Project risk management and design flexibility: Analysing a case and conditions of complementarity," Research Policy, Elsevier, vol. 40(3), pages 415-428, April.
    9. Christian Terwiesch & Yi Xu, 2008. "Innovation Contests, Open Innovation, and Multiagent Problem Solving," Management Science, INFORMS, vol. 54(9), pages 1529-1543, September.
    10. Kevin J. Boudreau & Nicola Lacetera & Karim R. Lakhani, 2011. "Incentives and Problem Uncertainty in Innovation Contests: An Empirical Analysis," Management Science, INFORMS, vol. 57(5), pages 843-863, May.
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    16. André Gygax & Anna Griffiths, 2007. "Do venture capitalists imitate portfolio size?," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 21(1), pages 69-94, March.
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    19. Friederike Wall, 2016. "Agent-based modeling in managerial science: an illustrative survey and study," Review of Managerial Science, Springer, vol. 10(1), pages 135-193, January.
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    21. Pierre-Antoine Arrighi & Pascal Le Masson & Benoit Weil, 2015. "Managing radical innovation as an innovative design process: generative constraints and cumulative sets of rules," Post-Print hal-01199932, HAL.
    22. Sanjiv Erat & Vish Krishnan, 2012. "Managing Delegated Search Over Design Spaces," Management Science, INFORMS, vol. 58(3), pages 606-623, March.
    23. Independent Evaluation Group, 2014. "Learning and Results in World Bank Operations : How the Bank Learns, Evaluation 1," World Bank Publications, The World Bank, number 19982.
    24. Gambardella, Alfonso, 2013. "The economic value of patented inventions: Thoughts and some open questions," International Journal of Industrial Organization, Elsevier, vol. 31(5), pages 626-633.
    25. Raul O. Chao & Stylianos Kavadias, 2008. "A Theoretical Framework for Managing the New Product Development Portfolio: When and How to Use Strategic Buckets," Management Science, INFORMS, vol. 54(5), pages 907-921, May.


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